In this script we conduct the estimation for the measure_marginal approach for a single given env = nethermind.

PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.

Expected a result file nethermind_pg_marginal_full5_c50_step1_shuffle_size_10.csv.

programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count sample_id run_id measure_total_time_ns        env program_id
## 1    ADD       27         0      1              10762.32 nethermind     ADD_27
## 2    ADD       27         0      2              11183.77 nethermind     ADD_27
## 3    ADD       27         0      3              10759.05 nethermind     ADD_27
## 4    ADD       27         0      4              11155.19 nethermind     ADD_27
## 5    ADD       27         0      5              10926.58 nethermind     ADD_27
## 6    ADD       27         0      6              11877.04 nethermind     ADD_27

Switch removed_outliers to FALSE to see the comparison.

boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

if (removed_outliers) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}

# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
  mix_model = normalmixEM(df$measure_total_time_ns)
  print(summary(mix_model))
  plot(mix_model,which=2)
  mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
  mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
  over_threshold = which(df$measure_total_time_ns > mode_midpoint)
  df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
    
  return(df)
}

# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
  if (missing(bimodal_opcodes)) {
    bimodal_opcodes = c()
  }
  if (missing(plots)) {
    plots = "scatter"
  }
  if (missing(use_median)) {
    use_median = FALSE
  }
  print(c(opcode, env))
  
  df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
  
  if (opcode %in% bimodal_opcodes) {
    par(mfrow=c(1,2))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    title(main=paste(env, opcode))
    # correct_bimodal plots the second plot inside
    df = correct_bimodal(df)
  }
  
  if (use_median) {
    f = median
  } else {
    f = mean
  }
  df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
  
  model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
  print(summary(model_mean))
  slope = model_mean$coefficients[['op_count']]
  stderr = summary(model_mean)$coefficients['op_count','Std. Error']
  
  if (plots == "scatter" | plots == "all") {
    par(mfrow=c(1,1))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    rounded_slope = round(slope, 3)
    rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
    rounded_stderr = round(stderr, 3)
    title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
    abline(model_mean, col="red")
  }
  if (plots == "diagnostics" | plots == "all") {
    par(mfrow=c(2,2))
    plot(model_mean)
  }
  list("slope" = slope, "stderr" = stderr)
}

extract_opcodes <- function() {
  unique(measurements$opcode)
}
all_opcodes = extract_opcodes()

# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')

Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.

for (opcode in all_opcodes) {
  boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}

Now we can investigate the linear regressions.

if (env == 'evmone') {
  bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
  bimodals = c()
}

for (opcode in all_opcodes) {
  estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
  estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}
## [1] "ADD"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -303.35 -117.39  -41.44   86.40  373.01 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10500.745     45.413  231.23 < 0.0000000000000002 ***
## op_count       17.541      1.565   11.21  0.00000000000000402 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 164.5 on 49 degrees of freedom
## Multiple R-squared:  0.7193, Adjusted R-squared:  0.7136 
## F-statistic: 125.6 on 1 and 49 DF,  p-value: 0.000000000000004021

## [1] "MUL"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -345.37  -96.56    9.06  125.95  352.66 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10426.802     46.325  225.08 <0.0000000000000002 ***
## op_count       47.157      1.597   29.53 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 167.9 on 49 degrees of freedom
## Multiple R-squared:  0.9468, Adjusted R-squared:  0.9457 
## F-statistic: 872.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SUB"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -335.2 -131.5  -27.5  135.2  326.4 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10482.212     46.956  223.24 < 0.0000000000000002 ***
## op_count       19.642      1.619   12.14 0.000000000000000223 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 170.1 on 49 degrees of freedom
## Multiple R-squared:  0.7504, Adjusted R-squared:  0.7453 
## F-statistic: 147.3 on 1 and 49 DF,  p-value: 0.0000000000000002231

## [1] "DIV"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -334.62 -124.77   14.53  101.63  665.94 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10349.40      48.16  214.90 <0.0000000000000002 ***
## op_count       24.92       1.66   15.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 174.5 on 49 degrees of freedom
## Multiple R-squared:  0.8214, Adjusted R-squared:  0.8178 
## F-statistic: 225.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SDIV"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -232.38  -70.69  -13.65   47.92  327.08 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10507.029     35.734  294.03 <0.0000000000000002 ***
## op_count       32.929      1.232   26.73 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 129.5 on 49 degrees of freedom
## Multiple R-squared:  0.9358, Adjusted R-squared:  0.9345 
## F-statistic: 714.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MOD"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -295.08 -170.60   28.06  109.81  457.97 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10445.468     53.044  196.92 <0.0000000000000002 ***
## op_count       22.625      1.828   12.37 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 192.2 on 49 degrees of freedom
## Multiple R-squared:  0.7576, Adjusted R-squared:  0.7526 
## F-statistic: 153.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SMOD"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -328.85 -133.22  -26.65  133.40  544.57 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10507.431     51.741  203.08 <0.0000000000000002 ***
## op_count       35.940      1.783   20.15 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 187.5 on 49 degrees of freedom
## Multiple R-squared:  0.8923, Adjusted R-squared:  0.8901 
## F-statistic: 406.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDMOD"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -349.65  -66.42   -1.88  100.27  368.19 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10494.888     42.404  247.50 <0.0000000000000002 ***
## op_count       38.599      1.462   26.41 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 153.6 on 49 degrees of freedom
## Multiple R-squared:  0.9344, Adjusted R-squared:  0.933 
## F-statistic: 697.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MULMOD"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -536.99 -187.22  -19.85  170.99  631.97 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10666.68      68.19  156.43 <0.0000000000000002 ***
## op_count      106.59       2.35   45.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 247.1 on 49 degrees of freedom
## Multiple R-squared:  0.9767, Adjusted R-squared:  0.9763 
## F-statistic:  2057 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EXP"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -817.7 -153.0  -40.1  169.9  668.9 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10971.84      76.60  143.24 <0.0000000000000002 ***
## op_count      204.52       2.64   77.46 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 277.5 on 49 degrees of freedom
## Multiple R-squared:  0.9919, Adjusted R-squared:  0.9917 
## F-statistic:  6001 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SIGNEXTEND" "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -457.91 -144.19   -8.03   92.86  502.23 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10538.039     53.581   196.7 <0.0000000000000002 ***
## op_count       27.700      1.847    15.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 194.1 on 49 degrees of freedom
## Multiple R-squared:  0.8211, Adjusted R-squared:  0.8175 
## F-statistic: 224.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "LT"         "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -521.57 -203.88  -71.98   83.01 2407.26 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10201.935    120.343  84.774 < 0.0000000000000002 ***
## op_count       26.201      4.148   6.316          0.000000076 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 436 on 49 degrees of freedom
## Multiple R-squared:  0.4488, Adjusted R-squared:  0.4375 
## F-statistic:  39.9 on 1 and 49 DF,  p-value: 0.00000007603

## [1] "GT"         "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -450.60 -110.12   -7.09   97.18  336.57 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10374.358     45.349  228.77 < 0.0000000000000002 ***
## op_count       16.927      1.563   10.83   0.0000000000000134 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 164.3 on 49 degrees of freedom
## Multiple R-squared:  0.7053, Adjusted R-squared:  0.6993 
## F-statistic: 117.3 on 1 and 49 DF,  p-value: 0.0000000000000134

## [1] "SLT"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -422.23 -100.49  -15.85   97.77  418.59 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10407.895     47.426  219.46 <0.0000000000000002 ***
## op_count       22.966      1.635   14.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 171.8 on 49 degrees of freedom
## Multiple R-squared:  0.8011, Adjusted R-squared:  0.7971 
## F-statistic: 197.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SGT"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -334.8 -128.8   11.6  108.2  525.6 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10394.034     48.682  213.51 <0.0000000000000002 ***
## op_count       22.962      1.678   13.68 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 176.4 on 49 degrees of freedom
## Multiple R-squared:  0.7926, Adjusted R-squared:  0.7884 
## F-statistic: 187.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EQ"         "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -310.201  -61.999    5.632   71.733  276.373 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10316.83      33.95  303.90 <0.0000000000000002 ***
## op_count       15.76       1.17   13.47 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123 on 49 degrees of freedom
## Multiple R-squared:  0.7873, Adjusted R-squared:  0.783 
## F-statistic: 181.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ISZERO"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -209.21 -105.20   -5.64   67.77  395.72 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10369.90      38.88 266.709 < 0.0000000000000002 ***
## op_count       11.02       1.34   8.222      0.0000000000875 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 140.9 on 49 degrees of freedom
## Multiple R-squared:  0.5798, Adjusted R-squared:  0.5712 
## F-statistic: 67.61 on 1 and 49 DF,  p-value: 0.00000000008754

## [1] "AND"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -387.69  -94.85  -23.23   76.32  518.14 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10381.419     43.121   240.8 <0.0000000000000002 ***
## op_count       34.486      1.486    23.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 156.2 on 49 degrees of freedom
## Multiple R-squared:  0.9166, Adjusted R-squared:  0.9149 
## F-statistic: 538.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "OR"         "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -238.03 -103.89  -11.26   84.55  408.22 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10458.68      41.20  253.88 <0.0000000000000002 ***
## op_count       32.12       1.42   22.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 149.3 on 49 degrees of freedom
## Multiple R-squared:  0.9126, Adjusted R-squared:  0.9108 
## F-statistic: 511.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "XOR"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -393.92 -101.86  -24.74   94.41  369.54 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10408.809     43.603  238.72 <0.0000000000000002 ***
## op_count       33.504      1.503   22.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 158 on 49 degrees of freedom
## Multiple R-squared:  0.9102, Adjusted R-squared:  0.9084 
## F-statistic: 496.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NOT"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -364.74 -119.21   23.24  106.02  334.46 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10364.41      41.77  248.16 <0.0000000000000002 ***
## op_count       25.02       1.44   17.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 151.3 on 49 degrees of freedom
## Multiple R-squared:  0.8605, Adjusted R-squared:  0.8576 
## F-statistic: 302.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "BYTE"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -328.57 -112.69  -22.09   74.55  447.19 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10326.57      47.85  215.79 < 0.0000000000000002 ***
## op_count       19.12       1.65   11.59   0.0000000000000012 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 173.4 on 49 degrees of freedom
## Multiple R-squared:  0.7328, Adjusted R-squared:  0.7273 
## F-statistic: 134.3 on 1 and 49 DF,  p-value: 0.000000000000001198

## [1] "SHL"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -476.43 -108.27   -2.58  103.93  534.00 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10499.140     51.989  201.95 <0.0000000000000002 ***
## op_count       34.549      1.792   19.28 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 188.4 on 49 degrees of freedom
## Multiple R-squared:  0.8835, Adjusted R-squared:  0.8811 
## F-statistic: 371.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHR"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -580.88 -117.54   16.89  143.30  352.96 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10579.521     47.963  220.58 <0.0000000000000002 ***
## op_count       29.517      1.653   17.85 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 173.8 on 49 degrees of freedom
## Multiple R-squared:  0.8668, Adjusted R-squared:  0.864 
## F-statistic: 318.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SAR"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -328.09 -121.00   13.47  109.79  423.67 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10473.428     48.353  216.60 <0.0000000000000002 ***
## op_count       36.747      1.667   22.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 175.2 on 49 degrees of freedom
## Multiple R-squared:  0.9084, Adjusted R-squared:  0.9066 
## F-statistic: 486.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDRESS"    "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -275.622  -69.146   -9.657   72.780  303.130 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8213.950     32.976  249.09 <0.0000000000000002 ***
## op_count      16.321      1.137   14.36 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 119.5 on 49 degrees of freedom
## Multiple R-squared:  0.808,  Adjusted R-squared:  0.804 
## F-statistic: 206.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ORIGIN"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -220.04  -69.56  -30.78   55.70  247.01 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8213.234     30.414  270.05 <0.0000000000000002 ***
## op_count      16.051      1.048   15.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 110.2 on 49 degrees of freedom
## Multiple R-squared:  0.8271, Adjusted R-squared:  0.8236 
## F-statistic: 234.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLER"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -229.71  -90.17    1.79   95.10  352.94 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8260.182     34.994  236.04 <0.0000000000000002 ***
## op_count      14.897      1.206   12.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 126.8 on 49 degrees of freedom
## Multiple R-squared:  0.7569, Adjusted R-squared:  0.7519 
## F-statistic: 152.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLVALUE"  "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -266.42 -112.13   29.91  120.31  290.73 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8213.245     40.097 204.836 < 0.0000000000000002 ***
## op_count      10.012      1.382   7.244        0.00000000278 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 145.3 on 49 degrees of freedom
## Multiple R-squared:  0.5171, Adjusted R-squared:  0.5073 
## F-statistic: 52.47 on 1 and 49 DF,  p-value: 0.000000002783

## [1] "CALLDATALOAD" "nethermind"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -305.50 -130.78   -8.54   99.03  492.22 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 12052.052     50.312  239.54 <0.0000000000000002 ***
## op_count       22.426      1.734   12.93 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 182.3 on 49 degrees of freedom
## Multiple R-squared:  0.7734, Adjusted R-squared:  0.7688 
## F-statistic: 167.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATASIZE" "nethermind"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -402.98  -94.89    8.07   75.94  399.85 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8269.891     38.185 216.574 < 0.0000000000000002 ***
## op_count      12.248      1.316   9.305     0.00000000000208 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 138.4 on 49 degrees of freedom
## Multiple R-squared:  0.6386, Adjusted R-squared:  0.6312 
## F-statistic: 86.59 on 1 and 49 DF,  p-value: 0.000000000002077

## [1] "CALLDATACOPY" "nethermind"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -361.95 -141.54  -32.63   86.76 1215.18 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11405.283     72.499  157.32 <0.0000000000000002 ***
## op_count      103.161      2.499   41.28 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 262.7 on 49 degrees of freedom
## Multiple R-squared:  0.9721, Adjusted R-squared:  0.9715 
## F-statistic:  1704 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODESIZE"   "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -236.18  -71.54    1.54   76.56  228.79 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8268.716     29.705  278.36 < 0.0000000000000002 ***
## op_count      12.097      1.024   11.81 0.000000000000000598 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 107.6 on 49 degrees of freedom
## Multiple R-squared:  0.7402, Adjusted R-squared:  0.7349 
## F-statistic: 139.6 on 1 and 49 DF,  p-value: 0.0000000000000005981

## [1] "CODECOPY"   "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -414.75 -204.99  -36.65  135.43  745.60 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11311.926     74.481  151.88 <0.0000000000000002 ***
## op_count      104.331      2.567   40.64 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 269.9 on 49 degrees of freedom
## Multiple R-squared:  0.9712, Adjusted R-squared:  0.9706 
## F-statistic:  1651 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASPRICE"   "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -290.669  -64.089   -5.319   78.323  205.128 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8225.685     29.542  278.44 < 0.0000000000000002 ***
## op_count      11.392      1.018   11.19  0.00000000000000426 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 107 on 49 degrees of freedom
## Multiple R-squared:  0.7187, Adjusted R-squared:  0.7129 
## F-statistic: 125.2 on 1 and 49 DF,  p-value: 0.00000000000000426

## [1] "RETURNDATASIZE" "nethermind"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -182.90  -93.33   14.97   73.70  258.48 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8340.834     31.810  262.21 <0.0000000000000002 ***
## op_count      18.184      1.096   16.58 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 115.3 on 49 degrees of freedom
## Multiple R-squared:  0.8488, Adjusted R-squared:  0.8457 
## F-statistic:   275 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATACOPY" "nethermind"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -720.40 -222.14  -86.43  165.00 1545.99 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 20266.751    116.488  173.98 <0.0000000000000002 ***
## op_count      108.967      4.015   27.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 422.1 on 49 degrees of freedom
## Multiple R-squared:  0.9376, Adjusted R-squared:  0.9363 
## F-statistic: 736.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "COINBASE"   "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -214.036  -68.886    4.526   90.659  211.913 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8269.085     29.051  284.64 <0.0000000000000002 ***
## op_count      18.169      1.001   18.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 105.3 on 49 degrees of freedom
## Multiple R-squared:  0.8704, Adjusted R-squared:  0.8678 
## F-statistic: 329.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "TIMESTAMP"  "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -265.166  -98.328   -2.111   84.028  303.058 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8318.416     35.731 232.808 < 0.0000000000000002 ***
## op_count      11.874      1.232   9.641    0.000000000000667 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 129.5 on 49 degrees of freedom
## Multiple R-squared:  0.6548, Adjusted R-squared:  0.6478 
## F-statistic: 92.95 on 1 and 49 DF,  p-value: 0.0000000000006673

## [1] "NUMBER"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -306.23  -95.94  -17.79   69.50  471.80 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)  8306.29      41.48 200.271 < 0.0000000000000002 ***
## op_count       12.26       1.43   8.578      0.0000000000253 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 150.3 on 49 degrees of freedom
## Multiple R-squared:  0.6003, Adjusted R-squared:  0.5921 
## F-statistic: 73.59 on 1 and 49 DF,  p-value: 0.00000000002528

## [1] "DIFFICULTY" "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -216.31  -69.41  -17.53   78.70  220.46 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8262.477     29.222  282.75 <0.0000000000000002 ***
## op_count      13.520      1.007   13.42 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 105.9 on 49 degrees of freedom
## Multiple R-squared:  0.7862, Adjusted R-squared:  0.7818 
## F-statistic: 180.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASLIMIT"   "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -271.038  -55.374   -1.162   60.481  246.127 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8302.3359    28.5684   290.6 <0.0000000000000002 ***
## op_count      13.5920     0.9847    13.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 103.5 on 49 degrees of freedom
## Multiple R-squared:  0.7954, Adjusted R-squared:  0.7912 
## F-statistic: 190.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CHAINID"    "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -362.82  -81.48   -6.27   86.75  302.73 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8299.636     37.903  218.97 <0.0000000000000002 ***
## op_count      19.819      1.306   15.17 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 137.3 on 49 degrees of freedom
## Multiple R-squared:  0.8244, Adjusted R-squared:  0.8209 
## F-statistic: 230.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SELFBALANCE" "nethermind" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -257.49  -64.01  -22.33   80.02  321.24 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  8351.62      32.49  257.02 <0.0000000000000002 ***
## op_count       35.93       1.12   32.08 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 117.7 on 49 degrees of freedom
## Multiple R-squared:  0.9546, Adjusted R-squared:  0.9536 
## F-statistic:  1029 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "POP"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -297.03  -75.49  -26.09   70.72  405.47 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9223.136     37.975 242.875 < 0.0000000000000002 ***
## op_count       5.022      1.309   3.836             0.000358 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 137.6 on 49 degrees of freedom
## Multiple R-squared:  0.231,  Adjusted R-squared:  0.2153 
## F-statistic: 14.72 on 1 and 49 DF,  p-value: 0.0003575

## [1] "MLOAD"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -286.14 -123.19  -12.35   90.72  449.88 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 12136.460     50.546  240.11 <0.0000000000000002 ***
## op_count       56.158      1.742   32.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 183.1 on 49 degrees of freedom
## Multiple R-squared:  0.955,  Adjusted R-squared:  0.954 
## F-statistic:  1039 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -361.5 -119.8  -14.3  110.1  560.6 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11113.086     52.558  211.44 <0.0000000000000002 ***
## op_count       48.596      1.812   26.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 190.4 on 49 degrees of freedom
## Multiple R-squared:  0.9362, Adjusted R-squared:  0.9349 
## F-statistic: 719.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE8"    "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -304.15 -108.01  -19.53  100.18  662.24 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11020.359     43.878  251.16 <0.0000000000000002 ***
## op_count       51.617      1.512   34.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 159 on 49 degrees of freedom
## Multiple R-squared:  0.9596, Adjusted R-squared:  0.9588 
## F-statistic:  1165 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMP"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -707.51  -73.16    9.88   77.34  347.46 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 9180.319     43.148  212.77 <0.0000000000000002 ***
## op_count      18.152      1.487   12.21 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 156.3 on 49 degrees of freedom
## Multiple R-squared:  0.7525, Adjusted R-squared:  0.7474 
## F-statistic:   149 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPI"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1441.20  -163.74    62.44   146.98   581.04 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11883.177     80.204 148.161 < 0.0000000000000002 ***
## op_count       27.045      2.765   9.783    0.000000000000415 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 290.6 on 49 degrees of freedom
## Multiple R-squared:  0.6614, Adjusted R-squared:  0.6545 
## F-statistic:  95.7 on 1 and 49 DF,  p-value: 0.0000000000004152

## [1] "PC"         "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -209.254  -67.861   -7.477   53.126  247.132 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8258.125     30.694 269.044 < 0.0000000000000002 ***
## op_count       8.610      1.058   8.138       0.000000000118 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 111.2 on 49 degrees of freedom
## Multiple R-squared:  0.5747, Adjusted R-squared:  0.5661 
## F-statistic: 66.23 on 1 and 49 DF,  p-value: 0.0000000001176

## [1] "MSIZE"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -271.386  -92.027   -0.934  100.864  261.673 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8272.160     35.439  233.42 < 0.0000000000000002 ***
## op_count      13.179      1.222   10.79   0.0000000000000153 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 128.4 on 49 degrees of freedom
## Multiple R-squared:  0.7037, Adjusted R-squared:  0.6977 
## F-statistic: 116.4 on 1 and 49 DF,  p-value: 0.00000000000001526

## [1] "GAS"        "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -199.849  -73.219   -7.827   76.941  275.525 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8261.289     30.326  272.42 < 0.0000000000000002 ***
## op_count      11.781      1.045   11.27  0.00000000000000328 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 109.9 on 49 degrees of freedom
## Multiple R-squared:  0.7216, Adjusted R-squared:  0.7159 
## F-statistic:   127 on 1 and 49 DF,  p-value: 0.000000000000003282

## [1] "JUMPDEST"   "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -751.01  -76.55   13.58  101.98  315.97 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 6828.869     43.839  155.77 < 0.0000000000000002 ***
## op_count       8.084      1.511    5.35           0.00000231 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 158.8 on 49 degrees of freedom
## Multiple R-squared:  0.3687, Adjusted R-squared:  0.3558 
## F-statistic: 28.62 on 1 and 49 DF,  p-value: 0.000002308

## [1] "PUSH1"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -227.38  -75.75  -23.20   82.01  249.22 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8145.283     30.545  266.66 < 0.0000000000000002 ***
## op_count      12.678      1.053   12.04 0.000000000000000297 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 110.7 on 49 degrees of freedom
## Multiple R-squared:  0.7474, Adjusted R-squared:  0.7423 
## F-statistic:   145 on 1 and 49 DF,  p-value: 0.0000000000000002975

## [1] "PUSH2"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -187.34  -99.05   11.66   76.07  274.45 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  8180.48      30.46  268.58 <0.0000000000000002 ***
## op_count       18.63       1.05   17.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 110.4 on 49 degrees of freedom
## Multiple R-squared:  0.8654, Adjusted R-squared:  0.8626 
## F-statistic:   315 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH3"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -288.72  -79.64   -7.41   56.14  343.18 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8248.276     32.657  252.57 <0.0000000000000002 ***
## op_count      16.361      1.126   14.53 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 118.3 on 49 degrees of freedom
## Multiple R-squared:  0.8117, Adjusted R-squared:  0.8079 
## F-statistic: 211.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH4"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -220.812  -76.958   -3.645   77.501  285.331 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  8228.45      31.03  265.17 <0.0000000000000002 ***
## op_count       16.79       1.07   15.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 112.4 on 49 degrees of freedom
## Multiple R-squared:  0.8341, Adjusted R-squared:  0.8307 
## F-statistic: 246.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH5"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -235.44  -79.31  -18.56   84.93  325.04 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8233.263     33.188  248.08 <0.0000000000000002 ***
## op_count      16.651      1.144   14.56 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 120.3 on 49 degrees of freedom
## Multiple R-squared:  0.8122, Adjusted R-squared:  0.8083 
## F-statistic: 211.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH6"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -221.079  -82.808   -0.808   91.105  214.682 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8254.485     31.301  263.71 <0.0000000000000002 ***
## op_count      16.342      1.079   15.15 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 113.4 on 49 degrees of freedom
## Multiple R-squared:  0.824,  Adjusted R-squared:  0.8204 
## F-statistic: 229.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH7"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -312.47  -67.50   -4.30   78.17  346.98 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  8221.79      35.97  228.55 <0.0000000000000002 ***
## op_count       17.56       1.24   14.16 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 130.3 on 49 degrees of freedom
## Multiple R-squared:  0.8036, Adjusted R-squared:  0.7996 
## F-statistic: 200.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH8"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -215.82  -75.19   26.76   60.26  262.13 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8303.846     31.672   262.2 <0.0000000000000002 ***
## op_count      13.646      1.092    12.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 114.8 on 49 degrees of freedom
## Multiple R-squared:  0.7613, Adjusted R-squared:  0.7564 
## F-statistic: 156.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH9"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -200.94  -85.96   11.08   72.33  270.50 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8286.566     31.381   264.1 <0.0000000000000002 ***
## op_count      15.250      1.082    14.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 113.7 on 49 degrees of freedom
## Multiple R-squared:  0.8022, Adjusted R-squared:  0.7982 
## F-statistic: 198.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH10"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -234.07  -83.60  -21.19   72.10  265.35 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8339.396     31.675  263.28 <0.0000000000000002 ***
## op_count      13.348      1.092   12.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 114.8 on 49 degrees of freedom
## Multiple R-squared:  0.7531, Adjusted R-squared:  0.7481 
## F-statistic: 149.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH11"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -327.35  -89.56   15.57   52.07  281.00 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8266.568     34.069  242.64 <0.0000000000000002 ***
## op_count      15.442      1.174   13.15 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123.4 on 49 degrees of freedom
## Multiple R-squared:  0.7792, Adjusted R-squared:  0.7747 
## F-statistic: 172.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH12"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -243.97  -82.43   11.79   94.63  211.04 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8258.327     33.613   245.7 <0.0000000000000002 ***
## op_count      15.642      1.159    13.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 121.8 on 49 degrees of freedom
## Multiple R-squared:  0.7881, Adjusted R-squared:  0.7838 
## F-statistic: 182.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH13"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -317.97  -84.24   -2.37   60.71  295.56 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8309.171     34.197  242.98 < 0.0000000000000002 ***
## op_count      12.758      1.179   10.82   0.0000000000000136 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123.9 on 49 degrees of freedom
## Multiple R-squared:  0.7051, Adjusted R-squared:  0.6991 
## F-statistic: 117.1 on 1 and 49 DF,  p-value: 0.00000000000001364

## [1] "PUSH14"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -219.01  -76.68   12.18   66.08  215.39 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8296.915     29.938   277.1 <0.0000000000000002 ***
## op_count      13.514      1.032    13.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108.5 on 49 degrees of freedom
## Multiple R-squared:  0.7778, Adjusted R-squared:  0.7733 
## F-statistic: 171.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH15"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -336.82  -81.91   22.00   82.37  222.96 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8256.868     36.727  224.82 < 0.0000000000000002 ***
## op_count      13.935      1.266   11.01  0.00000000000000755 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 133.1 on 49 degrees of freedom
## Multiple R-squared:  0.7121, Adjusted R-squared:  0.7062 
## F-statistic: 121.2 on 1 and 49 DF,  p-value: 0.000000000000007554

## [1] "PUSH16"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -274.69  -71.65   15.26   79.64  225.70 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8269.392     32.416  255.10 < 0.0000000000000002 ***
## op_count      13.203      1.117   11.82 0.000000000000000595 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 117.5 on 49 degrees of freedom
## Multiple R-squared:  0.7402, Adjusted R-squared:  0.7349 
## F-statistic: 139.6 on 1 and 49 DF,  p-value: 0.0000000000000005953

## [1] "PUSH17"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -288.012  -78.819   -7.902   72.041  249.472 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8309.496     34.158  243.27 < 0.0000000000000002 ***
## op_count      13.256      1.177   11.26  0.00000000000000341 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123.8 on 49 degrees of freedom
## Multiple R-squared:  0.7212, Adjusted R-squared:  0.7155 
## F-statistic: 126.8 on 1 and 49 DF,  p-value: 0.000000000000003405

## [1] "PUSH18"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -271.15  -82.81  -10.41   72.04  288.14 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8268.409     35.732  231.40 < 0.0000000000000002 ***
## op_count      13.866      1.232   11.26  0.00000000000000341 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 129.5 on 49 degrees of freedom
## Multiple R-squared:  0.7212, Adjusted R-squared:  0.7155 
## F-statistic: 126.8 on 1 and 49 DF,  p-value: 0.000000000000003405

## [1] "PUSH19"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -302.09  -58.71  -21.16   86.67  289.45 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8235.746     33.400  246.58 < 0.0000000000000002 ***
## op_count      13.558      1.151   11.78 0.000000000000000673 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 121 on 49 degrees of freedom
## Multiple R-squared:  0.7389, Adjusted R-squared:  0.7336 
## F-statistic: 138.7 on 1 and 49 DF,  p-value: 0.0000000000000006733

## [1] "PUSH20"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -339.96  -69.86  -11.60   59.22  328.44 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)  8317.96      36.84 225.778 < 0.0000000000000002 ***
## op_count       11.51       1.27   9.061     0.00000000000479 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 133.5 on 49 degrees of freedom
## Multiple R-squared:  0.6262, Adjusted R-squared:  0.6186 
## F-statistic:  82.1 on 1 and 49 DF,  p-value: 0.000000000004788

## [1] "PUSH21"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -257.59  -90.18  -30.43   85.19  250.98 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8342.736     35.206 236.967 < 0.0000000000000002 ***
## op_count      10.238      1.214   8.437      0.0000000000414 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 127.6 on 49 degrees of freedom
## Multiple R-squared:  0.5923, Adjusted R-squared:  0.5839 
## F-statistic: 71.18 on 1 and 49 DF,  p-value: 0.00000000004139

## [1] "PUSH22"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -269.89 -108.94    0.94  112.49  323.95 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8315.249     39.258   211.8 < 0.0000000000000002 ***
## op_count      11.637      1.353     8.6      0.0000000000235 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 142.2 on 49 degrees of freedom
## Multiple R-squared:  0.6015, Adjusted R-squared:  0.5933 
## F-statistic: 73.95 on 1 and 49 DF,  p-value: 0.00000000002349

## [1] "PUSH23"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -285.01 -113.13   -3.25  110.10  417.47 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8245.019     37.011  222.77 < 0.0000000000000002 ***
## op_count      13.601      1.276   10.66    0.000000000000023 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 134.1 on 49 degrees of freedom
## Multiple R-squared:  0.6988, Adjusted R-squared:  0.6926 
## F-statistic: 113.7 on 1 and 49 DF,  p-value: 0.00000000000002301

## [1] "PUSH24"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -420.76  -82.62  -11.30   91.71  219.12 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8346.865     35.418 235.669 < 0.0000000000000002 ***
## op_count      10.498      1.221   8.599      0.0000000000235 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 128.3 on 49 degrees of freedom
## Multiple R-squared:  0.6014, Adjusted R-squared:  0.5933 
## F-statistic: 73.95 on 1 and 49 DF,  p-value: 0.00000000002352

## [1] "PUSH25"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -298.11  -77.41   15.76   74.63  234.09 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8297.583     33.751 245.848 < 0.0000000000000002 ***
## op_count      11.252      1.163   9.672    0.000000000000602 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 122.3 on 49 degrees of freedom
## Multiple R-squared:  0.6563, Adjusted R-squared:  0.6492 
## F-statistic: 93.55 on 1 and 49 DF,  p-value: 0.0000000000006021

## [1] "PUSH26"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -163.82  -63.96  -13.94   51.46  268.41 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8320.0255    26.5811  313.00 <0.0000000000000002 ***
## op_count      11.6498     0.9162   12.71 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 96.31 on 49 degrees of freedom
## Multiple R-squared:  0.7674, Adjusted R-squared:  0.7627 
## F-statistic: 161.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH27"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -361.89  -97.53  -10.52   95.76  273.08 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8251.996     35.362  233.36 < 0.0000000000000002 ***
## op_count      12.511      1.219   10.26   0.0000000000000841 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 128.1 on 49 degrees of freedom
## Multiple R-squared:  0.6825, Adjusted R-squared:  0.6761 
## F-statistic: 105.4 on 1 and 49 DF,  p-value: 0.00000000000008411

## [1] "PUSH28"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -257.59  -83.16   -2.43   63.12  333.21 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8223.598     33.634  244.50 < 0.0000000000000002 ***
## op_count      13.279      1.159   11.45  0.00000000000000184 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 121.9 on 49 degrees of freedom
## Multiple R-squared:  0.7281, Adjusted R-squared:  0.7225 
## F-statistic: 131.2 on 1 and 49 DF,  p-value: 0.00000000000000184

## [1] "PUSH29"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -212.40  -69.94  -18.42   80.10  299.92 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8324.013     32.810 253.700 < 0.0000000000000002 ***
## op_count      10.403      1.131   9.198     0.00000000000299 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 118.9 on 49 degrees of freedom
## Multiple R-squared:  0.6333, Adjusted R-squared:  0.6258 
## F-statistic: 84.61 on 1 and 49 DF,  p-value: 0.00000000000299

## [1] "PUSH30"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -309.398  -84.923    7.114   84.736  314.625 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8343.707     36.286 229.940 < 0.0000000000000002 ***
## op_count       9.111      1.251   7.284        0.00000000241 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 131.5 on 49 degrees of freedom
## Multiple R-squared:  0.5199, Adjusted R-squared:  0.5101 
## F-statistic: 53.06 on 1 and 49 DF,  p-value: 0.00000000241

## [1] "PUSH31"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -268.01  -80.44  -14.91   78.96  255.07 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8265.152     32.270  256.12 < 0.0000000000000002 ***
## op_count      11.755      1.112   10.57   0.0000000000000311 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 116.9 on 49 degrees of freedom
## Multiple R-squared:  0.6951, Adjusted R-squared:  0.6888 
## F-statistic: 111.7 on 1 and 49 DF,  p-value: 0.00000000000003113

## [1] "PUSH32"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -247.736  -78.847   -3.202   89.197  230.260 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 8302.457     34.828 238.382 < 0.0000000000000002 ***
## op_count       8.976      1.201   7.477        0.00000000121 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 126.2 on 49 degrees of freedom
## Multiple R-squared:  0.5329, Adjusted R-squared:  0.5234 
## F-statistic: 55.91 on 1 and 49 DF,  p-value: 0.000000001214

## [1] "DUP1"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -313.16 -113.22   -2.37  105.95  375.86 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10248.696     45.678 224.370 < 0.0000000000000002 ***
## op_count        7.889      1.574   5.011           0.00000746 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 165.5 on 49 degrees of freedom
## Multiple R-squared:  0.3388, Adjusted R-squared:  0.3253 
## F-statistic: 25.11 on 1 and 49 DF,  p-value: 0.000007457

## [1] "DUP2"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -384.59 -111.29   18.53   96.35  332.79 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10302.321     45.117 228.348 < 0.0000000000000002 ***
## op_count        5.890      1.555   3.788             0.000416 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 163.5 on 49 degrees of freedom
## Multiple R-squared:  0.2265, Adjusted R-squared:  0.2107 
## F-statistic: 14.35 on 1 and 49 DF,  p-value: 0.0004162

## [1] "DUP3"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -313.05  -99.21    0.50   77.26  460.88 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10279.964     43.312 237.350 < 0.0000000000000002 ***
## op_count        6.373      1.493   4.269            0.0000897 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 156.9 on 49 degrees of freedom
## Multiple R-squared:  0.2711, Adjusted R-squared:  0.2562 
## F-statistic: 18.22 on 1 and 49 DF,  p-value: 0.00008975

## [1] "DUP4"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -325.25  -86.31  -14.52   76.85  691.97 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10334.746     45.753  225.88 < 0.0000000000000002 ***
## op_count        5.141      1.577    3.26              0.00203 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 165.8 on 49 degrees of freedom
## Multiple R-squared:  0.1782, Adjusted R-squared:  0.1615 
## F-statistic: 10.63 on 1 and 49 DF,  p-value: 0.00203

## [1] "DUP5"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -374.57  -62.85    9.37   83.02  259.81 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10335.091     34.987 295.394 < 0.0000000000000002 ***
## op_count        4.331      1.206   3.591             0.000761 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 126.8 on 49 degrees of freedom
## Multiple R-squared:  0.2084, Adjusted R-squared:  0.1922 
## F-statistic:  12.9 on 1 and 49 DF,  p-value: 0.0007608

## [1] "DUP6"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -344.33  -68.87    6.56   82.39  208.23 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10288.759     31.122  330.60 < 0.0000000000000002 ***
## op_count        5.932      1.073    5.53           0.00000123 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 112.8 on 49 degrees of freedom
## Multiple R-squared:  0.3843, Adjusted R-squared:  0.3717 
## F-statistic: 30.58 on 1 and 49 DF,  p-value: 0.000001229

## [1] "DUP7"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -342.01 -109.75   14.84   95.44  459.36 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10259.668     43.179 237.609 < 0.0000000000000002 ***
## op_count        8.593      1.488   5.774          0.000000521 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 156.5 on 49 degrees of freedom
## Multiple R-squared:  0.4049, Adjusted R-squared:  0.3927 
## F-statistic: 33.34 on 1 and 49 DF,  p-value: 0.0000005214

## [1] "DUP8"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -482.62  -76.25  -20.25  110.35  293.19 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10262.537     42.842 239.546 < 0.0000000000000002 ***
## op_count        8.528      1.477   5.775          0.000000519 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 155.2 on 49 degrees of freedom
## Multiple R-squared:  0.405,  Adjusted R-squared:  0.3928 
## F-statistic: 33.35 on 1 and 49 DF,  p-value: 0.000000519

## [1] "DUP9"       "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -287.363  -72.360    1.348   80.210  288.349 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10264.540     39.025 263.024 < 0.0000000000000002 ***
## op_count        6.487      1.345   4.822            0.0000142 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.4 on 49 degrees of freedom
## Multiple R-squared:  0.3218, Adjusted R-squared:  0.308 
## F-statistic: 23.25 on 1 and 49 DF,  p-value: 0.00001419

## [1] "DUP10"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -330.04 -121.50   17.40   76.47  360.99 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10326.012     43.432 237.750 < 0.0000000000000002 ***
## op_count        5.901      1.497   3.942             0.000257 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 157.4 on 49 degrees of freedom
## Multiple R-squared:  0.2407, Adjusted R-squared:  0.2252 
## F-statistic: 15.54 on 1 and 49 DF,  p-value: 0.000257

## [1] "DUP11"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -311.44  -84.31  -20.19   70.15  442.81 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10354.529     39.028 265.311 < 0.0000000000000002 ***
## op_count        5.869      1.345   4.362            0.0000661 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.4 on 49 degrees of freedom
## Multiple R-squared:  0.2797, Adjusted R-squared:  0.265 
## F-statistic: 19.03 on 1 and 49 DF,  p-value: 0.00006608

## [1] "DUP12"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -240.86 -114.45    0.72   83.38  326.98 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10257.632     39.920 256.956 < 0.0000000000000002 ***
## op_count        7.288      1.376   5.297           0.00000278 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 144.6 on 49 degrees of freedom
## Multiple R-squared:  0.3641, Adjusted R-squared:  0.3511 
## F-statistic: 28.05 on 1 and 49 DF,  p-value: 0.000002777

## [1] "DUP13"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -259.8 -116.2   -7.2  118.0  342.3 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10294.366     40.030 257.168 < 0.0000000000000002 ***
## op_count        6.746      1.380   4.889            0.0000113 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 145 on 49 degrees of freedom
## Multiple R-squared:  0.3279, Adjusted R-squared:  0.3142 
## F-statistic:  23.9 on 1 and 49 DF,  p-value: 0.0000113

## [1] "DUP14"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -289.38 -124.97   20.40   88.08  344.34 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10223.069     44.058 232.038 < 0.0000000000000002 ***
## op_count        9.603      1.519   6.323         0.0000000742 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 159.6 on 49 degrees of freedom
## Multiple R-squared:  0.4493, Adjusted R-squared:  0.4381 
## F-statistic: 39.98 on 1 and 49 DF,  p-value: 0.00000007415

## [1] "DUP15"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -319.41  -99.98    4.54   93.41  413.35 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10326.803     39.681 260.245 < 0.0000000000000002 ***
## op_count        6.787      1.368   4.962           0.00000881 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 143.8 on 49 degrees of freedom
## Multiple R-squared:  0.3344, Adjusted R-squared:  0.3208 
## F-statistic: 24.62 on 1 and 49 DF,  p-value: 0.000008813

## [1] "DUP16"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -334.05 -107.60  -18.68   82.71  389.77 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10361.682     42.958 241.208 < 0.0000000000000002 ***
## op_count        8.156      1.481   5.508           0.00000133 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 155.7 on 49 degrees of freedom
## Multiple R-squared:  0.3824, Adjusted R-squared:  0.3698 
## F-statistic: 30.34 on 1 and 49 DF,  p-value: 0.000001326

## [1] "SWAP1"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -373.39 -119.48  -15.61   96.11  352.28 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9342.108     46.220 202.122 < 0.0000000000000002 ***
## op_count       7.282      1.593   4.571            0.0000331 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 167.5 on 49 degrees of freedom
## Multiple R-squared:  0.2989, Adjusted R-squared:  0.2846 
## F-statistic: 20.89 on 1 and 49 DF,  p-value: 0.00003308

## [1] "SWAP2"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -243.476  -63.332   -3.653   59.072  285.431 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9239.467     29.617 311.962 < 0.0000000000000002 ***
## op_count       9.778      1.021   9.578    0.000000000000825 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 107.3 on 49 degrees of freedom
## Multiple R-squared:  0.6518, Adjusted R-squared:  0.6447 
## F-statistic: 91.74 on 1 and 49 DF,  p-value: 0.000000000000825

## [1] "SWAP3"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -231.61  -79.96   -1.78   79.02  324.10 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9256.748     34.945 264.898 < 0.0000000000000002 ***
## op_count       9.520      1.205   7.904       0.000000000268 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 126.6 on 49 degrees of freedom
## Multiple R-squared:  0.5604, Adjusted R-squared:  0.5514 
## F-statistic: 62.47 on 1 and 49 DF,  p-value: 0.0000000002683

## [1] "SWAP4"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -296.76  -70.67  -16.15   86.24  257.94 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9288.778     33.539 276.958 < 0.0000000000000002 ***
## op_count       9.041      1.156   7.821       0.000000000359 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 121.5 on 49 degrees of freedom
## Multiple R-squared:  0.5552, Adjusted R-squared:  0.5461 
## F-statistic: 61.17 on 1 and 49 DF,  p-value: 0.0000000003594

## [1] "SWAP5"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -314.68  -87.68  -22.22  114.18  262.48 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9325.489     35.013 266.347 < 0.0000000000000002 ***
## op_count       6.663      1.207   5.521           0.00000127 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 126.9 on 49 degrees of freedom
## Multiple R-squared:  0.3835, Adjusted R-squared:  0.3709 
## F-statistic: 30.48 on 1 and 49 DF,  p-value: 0.000001267

## [1] "SWAP6"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -248.97  -91.41    1.25   89.26  390.54 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9350.897     37.800 247.380 < 0.0000000000000002 ***
## op_count       8.279      1.303   6.354         0.0000000665 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 137 on 49 degrees of freedom
## Multiple R-squared:  0.4517, Adjusted R-squared:  0.4405 
## F-statistic: 40.37 on 1 and 49 DF,  p-value: 0.00000006651

## [1] "SWAP7"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -206.680  -70.638   -1.938   80.314  246.966 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9295.734     30.249  307.31 < 0.0000000000000002 ***
## op_count      10.659      1.043   10.22   0.0000000000000962 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 109.6 on 49 degrees of freedom
## Multiple R-squared:  0.6808, Adjusted R-squared:  0.6743 
## F-statistic: 104.5 on 1 and 49 DF,  p-value: 0.00000000000009623

## [1] "SWAP8"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -269.96  -78.03  -15.70   52.70  444.26 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9273.866     37.367 248.183 < 0.0000000000000002 ***
## op_count       9.173      1.288   7.122         0.0000000043 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 135.4 on 49 degrees of freedom
## Multiple R-squared:  0.5086, Adjusted R-squared:  0.4986 
## F-statistic: 50.72 on 1 and 49 DF,  p-value: 0.000000004301

## [1] "SWAP9"      "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -221.296  -71.806    8.203   55.715  188.522 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9323.7892    26.2656  354.98 < 0.0000000000000002 ***
## op_count       8.1571     0.9054    9.01      0.0000000000057 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 95.17 on 49 degrees of freedom
## Multiple R-squared:  0.6236, Adjusted R-squared:  0.6159 
## F-statistic: 81.18 on 1 and 49 DF,  p-value: 0.000000000005698

## [1] "SWAP10"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -193.44  -69.82  -15.66   79.56  271.78 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9351.937     32.109 291.252 < 0.0000000000000002 ***
## op_count      10.035      1.107   9.067     0.00000000000469 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 116.3 on 49 degrees of freedom
## Multiple R-squared:  0.6265, Adjusted R-squared:  0.6189 
## F-statistic:  82.2 on 1 and 49 DF,  p-value: 0.000000000004693

## [1] "SWAP11"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -325.75  -82.67  -12.84   63.57  422.65 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9272.956     36.479 254.198 < 0.0000000000000002 ***
## op_count       8.959      1.257   7.125        0.00000000424 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 132.2 on 49 degrees of freedom
## Multiple R-squared:  0.5089, Adjusted R-squared:  0.4988 
## F-statistic: 50.77 on 1 and 49 DF,  p-value: 0.000000004244

## [1] "SWAP12"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -272.06 -108.33   -2.88   80.19  421.82 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9329.727     41.852 222.921 < 0.0000000000000002 ***
## op_count       9.617      1.443   6.667         0.0000000218 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 151.6 on 49 degrees of freedom
## Multiple R-squared:  0.4756, Adjusted R-squared:  0.4649 
## F-statistic: 44.44 on 1 and 49 DF,  p-value: 0.0000000218

## [1] "SWAP13"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -425.84 -102.35    6.37   96.81  411.15 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9329.302     44.484 209.723 < 0.0000000000000002 ***
## op_count       8.333      1.533   5.435           0.00000171 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 161.2 on 49 degrees of freedom
## Multiple R-squared:  0.3761, Adjusted R-squared:  0.3634 
## F-statistic: 29.54 on 1 and 49 DF,  p-value: 0.000001715

## [1] "SWAP14"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -251.00  -90.91   -1.72   90.93  332.90 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9317.011     37.826 246.314 < 0.0000000000000002 ***
## op_count       7.631      1.304   5.853          0.000000395 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 137.1 on 49 degrees of freedom
## Multiple R-squared:  0.4114, Adjusted R-squared:  0.3994 
## F-statistic: 34.25 on 1 and 49 DF,  p-value: 0.0000003946

## [1] "SWAP15"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -223.80  -98.53  -13.86   91.71  499.27 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9415.040     37.526 250.897 < 0.0000000000000002 ***
## op_count       8.596      1.293   6.646         0.0000000235 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 136 on 49 degrees of freedom
## Multiple R-squared:  0.474,  Adjusted R-squared:  0.4633 
## F-statistic: 44.16 on 1 and 49 DF,  p-value: 0.0000000235

## [1] "SWAP16"     "nethermind"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -276.895  -91.191    9.917   77.807  262.051 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 9375.487     36.399 257.577 < 0.0000000000000002 ***
## op_count       9.437      1.255   7.522        0.00000000104 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 131.9 on 49 degrees of freedom
## Multiple R-squared:  0.5359, Adjusted R-squared:  0.5264 
## F-statistic: 56.58 on 1 and 49 DF,  p-value: 0.000000001036

Export the results

write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)